function score_res = knn_predict( model , feat_test)

feat_train = model.feat_train;

n_train = size(feat_train,1);
n_test = size(feat_test,1);

dist_mat = arrayfun( @(i)( sum(abs( ...
                        feat_train - repmat( feat_test(i,:), n_train,1))...
                        ,2)),...
                        1:n_test,...
                        'UniformOutput',false);

dim = size(model.score_train,2);

score_res = zeros( n_test, dim );

knn = 5;
                    
for i = 1:n_test
    [~,sort_id] = sort( dist_mat{i});
    sort_id = sort_id(1:knn);
    score_res(i,:) = mean(model.score_train(sort_id,:));
end